Package: lmtp 1.5.0

lmtp: Non-Parametric Causal Effects of Feasible Interventions Based on Modified Treatment Policies

Non-parametric estimators for casual effects based on longitudinal modified treatment policies as described in Diaz, Williams, Hoffman, and Schenck <doi:10.1080/01621459.2021.1955691>, traditional point treatment, and traditional longitudinal effects. Continuous, binary, categorical treatments, and multivariate treatments are allowed as well are censored outcomes. The treatment mechanism is estimated via a density ratio classification procedure irrespective of treatment variable type. For both continuous and binary outcomes, additive treatment effects can be calculated and relative risks and odds ratios may be calculated for binary outcomes. Supports survival outcomes with competing risks (Diaz, Hoffman, and Hejazi; <doi:10.1007/s10985-023-09606-7>).

Authors:Nicholas Williams [aut, cre, cph], Iván Díaz [aut, cph]

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lmtp.pdf |lmtp.html
lmtp/json (API)
NEWS

# Install 'lmtp' in R:
install.packages('lmtp', repos = c('https://nt-williams.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/nt-williams/lmtp/issues

Datasets:

On CRAN:

Conda-Forge:

causal-inferencecensored-datalongitudinal-datamachine-learningmodified-treatment-policynonparametric-statisticsprecision-medicinerobust-statisticsstatisticsstochastic-interventionssurvival-analysistargeted-learning

6.36 score 63 stars 91 scripts 559 downloads 13 exports 35 dependencies

Last updated 9 hours agofrom:17cbb2149d (on devel). Checks:6 OK, 3 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 06 2025
R-4.5-winNOTEMar 06 2025
R-4.5-macNOTEMar 06 2025
R-4.5-linuxNOTEMar 06 2025
R-4.4-winOKMar 06 2025
R-4.4-macOKMar 06 2025
R-4.4-linuxOKMar 06 2025
R-4.3-winOKMar 06 2025
R-4.3-macOKMar 06 2025

Exports:create_node_listevent_locfipsilmtp_contrastlmtp_controllmtp_ipwlmtp_sdrlmtp_sublmtp_survivallmtp_tmlestatic_binary_offstatic_binary_ontidy

Dependencies:abindassertthatbackportsbitopscaToolscheckmateclicodetoolscvAUCdata.tabledigestforeachfuturefuture.applygamgenericsglobalsgluegplotsgtoolsifeisotoneiteratorsKernSmoothlifecyclelistenvnnlsorigamiparallellyprogressrR6rlangROCRS7SuperLearner